Automatically Selecting a Suitable Integration Scheme for Systems of Differential Equations in Neuron Models

On the level of the spiking activity, the integrate-and-fire neuron is one of the most commonly used descriptions of neural activity. A multitude of variants has been proposed to cope with the huge diversity of behaviors observed in biological nerve cells. The main appeal of this class of model is that it can be defined in terms of a hybrid model, where a set of mathematical equations describes the sub-threshold dynamics of the membrane potential and the generation of action potentials is often only added algorithmically without the shape of spikes being part of the equations. In contrast to more detailed biophysical models, this simple description of neuron models allows the routine simulation of large biological neuronal networks on standard hardware widely available in most laboratories these days. The time evolution of the relevant state variables is usually defined by a small set of ordinary differential equations (ODEs). A small number of evolution schemes for the corresponding systems of ODEs are commonly used for many neuron models, and form the basis of the neuron model implementations built into commonly used simulators like Brian, NEST and NEURON. However, an often neglected problem is that the implemented evolution schemes are only rarely selected through a structured process based on numerical criteria. This practice cannot guarantee accurate and stable solutions for the equations and the actual quality of the solution depends largely on the parametrization of the model. In this article, we give an overview of typical equations and state descriptions for the dynamics of the relevant variables in integrate-and-fire models. We then describe a formal mathematical process to automate the design or selection of a suitable evolution scheme for this large class of models. Finally, we present the reference implementation of our symbolic analysis toolbox for ODEs that can guide modelers during the implementation of custom neuron models

Simulations on Consumer Tests: Systematic Evaluation of Tolerance Ranges by Model-Based Generation of Simulation Scenarios

Context: Since 2014 several modern cars were rated regarding the performances of their active safety systems at the European New Car Assessment Programme (EuroNCAP). Nowadays, consumer tests play a significant role for the OEM's series development with worldwide perspective, because a top rating is needed to underline the worthiness of active safety features from the customers' point of view. Furthermore, EuroNCAP already published their roadmap 2020 in which they outline further extensions in today's testing and rating procedures that will aggravate the current requirements addressed to those systems. Especially Autonomous Emergency Braking/Forward Collision Warning systems (AEB/FCW) are going to face a broader field of application as pedestrian detection or two-way traffic scenarios. Objective: This work focuses on the systematic generation of test scenarios concentrating on specific parameters that can vary within certain tolerance ranges like the lateral position of the vehicle-under-test (VUT) and its test velocity for example. It is of high interest to examine the effect of the tolerance ranges on the braking points in different test cases representing different trajectories and velocities because they will influence significantly a later scoring during the assessments and thus the safety abilities of the regarding car. Method: We present a formal model using a graph to represent the allowed variances based on the relevant points in time. Now, varying velocities of the VUT will be added to the model while the vehicle is approaching a target vehicle. The derived trajectories were used as test cases for a simulation environment. Selecting interesting test cases and processing them with the simulation environment, the influence on the system's performance of different test parameters will be investigated.Comment: 15 pages, 6 figures, Fahrerassistenzsysteme und Integrierte Sicherheit, VDI Berichte 2014, pp. 403-41

Report on the Aachen OCL meeting

As a continuation of the OCL workshop during the MODELS 2013 conference in October 2013, a number of OCL experts decided to meet in November 2013 in Aachen for two days to discuss possible short term improvements of OCL for an upcoming OMG meeting and to envision possible future long-term developments of the language. This paper is a sort of "minutes of the meeting" and intended to quickly inform the OCL community about the discussion topics

NESTML - die domänenspezifische Sprache für den NEST-Simulator neuronaler Netzwerke im Human Brain Project, 17

Domänenspezifische Sprachen erlauben gegenüber General Purpose Programmiersprachen begrenzten und problemorientierten Funktionsumfang an. Verschiedene Modellierungssprachen für die Computational Neuroscience wurden bereits vorgeschlagen. Da diese Sprachen jedoch typischerweise Simulatorunabhängigkeit anstreben, unterstützen sie oft nur eine Untermenge der vom Modellierer gewünschten Eigenschaften.Diese Arbeit präsentiert den Entwurf und die Implementierung der modularen und erweiterbaren domänenspezifischen Sprache NESTML, die Konzepte aus den Neurowissenschaften als vollwertige Sprachkonstrukte zur Verfügung stellt und Neurowissenschaftler so bei der Erstellung von Neuronemodellen für das neuronale Simulationswerkzeug NEST unterstützt.NESTML wurde mithilfe von MontiCore entwickelt. MontiCore ist eine Language Workbench zur Erstellung von domänenspezifischen Sprachen. MontiCore verwendet und erweitert das Grammatikformat von ANTLR4, das auf dem EBNF-Formalismus basiert, um zusätzliche Konzepte für die Grammatikwiederverwendung. MontiCore stellt eine modulare Infrastruktur für das Parsen von Modellen, den Aufbau der Symboltabllen und zum Prüfen der Kontextbedingungen bereit. Damit können die Entwicklungskosten von NESTML signifikant gesenkt werden

NESTML - die domänenspezifische Sprache für den NEST-Simulator neuronaler Netzwerke im Human Brain Project

Domänenspezifische Sprachen erlauben gegenüber General Purpose Programmiersprachen begrenzten und problemorientierten Funktionsumfang an. Verschiedene Modellierungssprachen für die Computational Neuroscience wurden bereits vorgeschlagen. Da diese Sprachen jedoch typischerweise Simulatorunabhängigkeit anstreben, unterstützen sie oft nur eine Untermenge der vom Modellierer gewünschten Eigenschaften.Diese Arbeit präsentiert den Entwurf und die Implementierung der modularen und erweiterbaren domänenspezifischen Sprache NESTML, die Konzepte aus den Neurowissenschaften als vollwertige Sprachkonstrukte zur Verfügung stellt und Neurowissenschaftler so bei der Erstellung von Neuronemodellen für das neuronale Simulationswerkzeug NEST unterstützt.NESTML wurde mithilfe von MontiCore entwickelt. MontiCore ist eine Language Workbench zur Erstellung von domänenspezifischen Sprachen. MontiCore verwendet und erweitert das Grammatikformat von ANTLR4, das auf dem EBNF-Formalismus basiert, um zusätzliche Konzepte für die Grammatikwiederverwendung. MontiCore stellt eine modulare Infrastruktur für das Parsen von Modellen, den Aufbau der Symboltabllen und zum Prüfen der Kontextbedingungen bereit. Damit können die Entwicklungskosten von NESTML signifikant gesenkt werden

NESTML Tutorial

The fast advancements in neuroscience and the multitude of newly developed neuron models require a language to describe neurons by means of equations and state transitions. The capability of implementing new models in precise agreement with their mathematical definition and subsequently running computationally efficient simulations is essential for progress on the theoretical foundation of neuroscience. The use of abstract modelling languages like NESTML enables neuroscientists to formally specify their models and to automatically produce high level simulation code that runs efficiently on various computer architectures. The workshop provides students with concepts and hands on experience in this language to facilitate a seamless workflow between theory and simulation

NESTML: a modeling language for spiking neurons

Biological nervous systems exhibit astonishing complexity. Neuroscientists aim to capture this complexityby modeling and simulation of biological processes. Often very complex models are necessaryto depict the processes, which makes it difficult to create these models. Powerful tools arethus necessary, which enable neuroscientists to express models in a comprehensive and concise wayand generate efficient code for digital simulations. Several modeling languages for computationalneuroscience have been proposed [Gl10, Ra11]. However, as these languages seek simulator independencethey typically only support a subset of the features desired by the modeler. In this article,we present the modular and extensible domain specific language NESTML, which provides neurosciencedomain concepts as first-class language constructs and supports domain experts in creatingneuron models for the neural simulation tool NEST. NESTML and a set of example models arepublically available on GitHub